Bayesian Analysis of Constant Elasticity of Variance Models

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Show simple item record Chan, JS Choy, S Lee, AB 2009-12-21T02:27:56Z 2007-01
dc.identifier.citation Applied Stochastic Models in Business and Industry, 2007, 23 pp. 83 - 96
dc.identifier.issn 1524-1904
dc.identifier.other C1 en_US
dc.description.abstract This paper focuses on the estimation of some models in finance and in particular, in interest rates. We analyse discretized versions of the constant elasticity of variance (CEV) models where the normal law showing up in the usual discretization of the diffusion part is replaced by a range of heavy-tailed distributions. A further extension of the model is to allow the elasticity of variance to be a parameter itself. This generalized model allows great flexibility in modelling and simplifies the model implementation considerably using the scale mixtures representation. The mixing parameters provide a means to identify possible outliers and protect inference by down-weighting the distorting effects of these outliers. For parameter estimation, Bayesian approach is adopted and implemented using the software WinBUGS (Bayesian inference using Gibbs sampler). Results from a real data analysis show that an exponential power distribution with a random shape parameter, which is highly leptokurtic compared with the normal distribution, forms the best CEV model for the data
dc.publisher John Wiley & Sons,Ltd
dc.relation.isbasedon 10.1002/asmb.639
dc.title Bayesian Analysis of Constant Elasticity of Variance Models
dc.type Journal Article
dc.parent Applied Stochastic Models in Business and Industry
dc.journal.volume 23
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 83 en_US
dc.identifier.endpage 96 en_US SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 0104 Statistics
dc.for 0102 Applied Mathematics
dc.personcode 030715
dc.percentage 50 en_US Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.description.keywords constant elasticity of variance (CEV) models, Bayesian inference, Gibbs sampler, Brownian motion en_US
dc.description.keywords Animals
dc.description.keywords Cryptosporidium
dc.description.keywords Campylobacter
dc.description.keywords Escherichia coli
dc.description.keywords Likelihood Functions
dc.description.keywords Water Microbiology
dc.description.keywords Fresh Water
dc.description.keywords Water Movements
dc.description.keywords Water Supply
dc.description.keywords Australia
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Uncategorised (ID: 363)
utslib.collection.history Closed (ID: 3)

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